{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 以 jieba 與 gensim 探索文本主題\n",
    "\n",
    "Note: 原文出自[Youngmi huang在Medium](https://medium.com/pyladies-taiwan/%E4%BB%A5-jieba-%E8%88%87-gensim-%E6%8E%A2%E7%B4%A2%E6%96%87%E6%9C%AC%E4%B8%BB%E9%A1%8C-%E4%BA%94%E6%9C%88%E5%A4%A9%E4%BA%BA%E7%94%9F%E7%84%A1%E9%99%90%E5%85%AC%E5%8F%B8%E6%AD%8C%E8%A9%9E%E5%88%86%E6%9E%90-i-cd2147b89083)的文章\n",
    "\n",
    "## 五月天人生無限公司歌詞分析 ( I )\n",
    "\n",
    "構成語句最基本為字詞，使用 word2vec 將字詞轉為詞向量後，為了從文本數據得到顯著而有用的資訊，需要的是探索支援自然語言處理的工具與嘗試，例如基於 python 的主題模型函式庫 gensim ，是 python 做自然語言處理必備的工具之一。\n",
    "\n",
    "本次主題著重於實現心目中想做的數據集的運用，會拆成上下篇，上篇主要為 jieba 的中文斷詞與特徵萃取；下篇則會聚焦於 gensim 主題建模的使用。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 源起\n",
    "\n",
    "五月天在 2017 年底的「人生無限公司演唱會」，在 10 月開搶後秒殺，短短15 分鐘完售 24 萬張門票，為什麼大家這麼喜歡搶五月天的門票？曾經有人說：每個人心目中都有一首五月天，我將它解讀為，每個人腦海中「第一首浮現的五月天」，那一首歌，陪伴人們成長過程的青春、夢想與回憶等。\n",
    "\n",
    "本次僅以「歌詞」作為相近歌單之間的主題探索，歌詞為文本，是屬於自然語言處理領域，若要做到更精細，可以結合文本+語音的方式去找尋歌曲相似度，此任務就會較為複雜。\n",
    "\n",
    "![五月天人生無限公司](https://i1.wp.com/www.iwaishin.com/wp-content/uploads/2017/12/img_1634.jpg?resize=696%2C478&ssl=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 為什麼要做這件事? 怎麼應用?\n",
    "\n",
    "出於好奇「人生無限公司演唱會」歌單裡的歌詞有哪些關鍵字詞，掌握這些字詞之後，在和五月天以及全場聽眾一起唱歌的時候，就可以避免不會唱的窘境( e.g. 派對動物的關鍵字詞: oh oh oh oh) 。\n",
    "\n",
    "另外，演唱會進行中，我們並不知道會有什麼驚喜，也許唱了一首冷門歌曲而你很喜歡，記下歌名之後，就可以透過主題建模找尋相近歌詞的方式，找到另一首你可能也很喜歡的歌曲。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 歌詞數據集\n",
    "\n",
    "蒐集了在 2017/9 ~ 2017/10 之間，五月天人生無限公司巡迴演唱會的歌單(於深圳、洛陽、南京、福州這 4 場)，共 33 首歌詞 (取自：[Moji 魔鏡歌詞網](https://mojim.com/))。\n",
    "\n",
    "數據集選取是保有彈性的，每個人可自由選擇喜歡的歌詞做資料蒐集 ( e.g. 蘇打綠、周杰倫、玖壹壹)。但在資料前處理的時候會需要一點時間(包含: 資料格式、特殊字元、統一英文大小寫、以及歌詞雜訊等)，這次花了不少時間在這塊以及嘗試讀取資料(這又是另一段故事了)，建議將資料整理成下列格式：\n",
    "\n",
    "![test](https://cdn-images-1.medium.com/max/800/1*SY-TRGJ-ZjUa8WoksESJMQ.png)\n",
    "\n",
    "同一首歌詞之間是空白、不同首歌詞之間是換行、存成文字檔 (預設編碼是 utf-8 )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 任務說明\n",
    "\n",
    "因 python3 對於中文支持與編碼更友善，本篇以 python3 進行。所需安裝套件：[jieba](https://github.com/fxsjy/jieba)、[gensim](https://radimrehurek.com/gensim/tutorial.html)、[wordcloud](https://github.com/amueller/word_cloud) (文字雲)。\n",
    "\n",
    "* 歌詞分析：較為初級的任務，利用 `jieba.analyse`取出文章中的關鍵詞，透過文字雲做視覺化呈現 。\n",
    "* 主題建模：引用 `gensim` 當中 `corpora` 、 `models`、 `similarities` 模組，分別做語料庫建立、主題模型、與相似度比較。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 專案的檔案路徑佈局\n",
    "\n",
    "1. 安裝Anaconda\n",
    "2. 產生一個Anaconda環境\n",
    "3. 安裝函式庫\n",
    "```\n",
    "pip install jieba\n",
    "pip install gensim\n",
    "pip install wordcloud\n",
    "```\n",
    "4. 下載 [youngmihuang/lyrics_application](https://github.com/youngmihuang/lyrics_application) 的Github專案\n",
    "```\n",
    "git clone https://github.com/youngmihuang/lyrics_application.git\n",
    "```\n",
    "\n",
    "5. 以\"Download ZIP\"的方式下載 [fxsjy/jieba](https://github.com/fxsjy/jieba), 解壓縮後將\"extra_dict\"整個目錄複製到\"lyrics_application/model\"目錄下\n",
    "\n",
    "6. 下載[Google 中文字型-Noto Sans CJK TC](https://noto-website-2.storage.googleapis.com/pkgs/NotoSansCJKtc-hinted.zip), 解壓縮後將\"NotoSansCJKtc-Black.otf\"字型檔複製到\"lyrics_application/fonts\"目錄下\n",
    "\n",
    "7. 最後你的目錄結構看起來像這樣: (這裡只列出來在這個範例會用到的相關檔案與目錄)\n",
    "\n",
    "```\n",
    "lyrics_application/\n",
    "├──xxxx.ipynb (代表這個範例的notebook)\n",
    "├── fonts/\n",
    "│   └── NotoSansCJKtc-Black.otf\n",
    "├── model/\n",
    "│   └── extra_dict\n",
    "│       ├── dict.txt.big\n",
    "│       ├── dict.txt.small\n",
    "│       ├── idf.txt.big\n",
    "│       └── stop_words.txt\n",
    "└── dataset/\n",
    "    ├── lyrics_cut_mayday.dataset\n",
    "    ├── lyrics_word_net_mayday.dataset\n",
    "    ├── mayday.txt\n",
    "    ├── single_song.txt\t\n",
    "    └── test1.txt\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 載入相關函式庫"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 把一些警告的訊息暫時関掉\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "\n",
    "# Utilities相關函式庫\n",
    "import os\n",
    "import numpy as np\n",
    "import codecs\n",
    "\n",
    "# 使用\"結巴(jieba)來進行分析\n",
    "import jieba.analyse\n",
    "\n",
    "# 圖像處理/展現的相關函式庫\n",
    "import matplotlib.pyplot as plt\n",
    "from wordcloud import WordCloud"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 設定相關設定與參數"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 專案的根目錄路徑\n",
    "ROOT_DIR = os.getcwd()\n",
    "\n",
    "# 訓練/驗證用的資料目錄\n",
    "DATASET_PATH = os.path.join(ROOT_DIR, \"dataset\")\n",
    "\n",
    "# 模型資料目錄\n",
    "MODEL_PATH = os.path.join(ROOT_DIR, \"model\")\n",
    "JIEBA_DICTFILE_PATH = os.path.join(MODEL_PATH,\"extra_dict\", \"dict.txt.big\")\n",
    "\n",
    "# 中文字型目錄\n",
    "FONT_PATH = os.path.join(ROOT_DIR, \"fonts\")\n",
    "FONT_FILE_PATH = os.path.join(FONT_PATH, \"NotoSansCJKtc-Black.otf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 歌詞分析\n",
    "\n",
    "歌詞分析主要用到的是 `jieba.analyse.extract_tags`，背後的原理為 `tf-idf` 算法。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. tf - idf 簡介 ( term frequency–inverse document frequency )\n",
    "\n",
    "`tf-idf` 是一種統計方法，此原理為評估一個字詞對於一個檔案集，或一個語料庫中的其中一份檔案的重要程度，這個概念十分重要。\n",
    "* tf (term frequency )：詞頻，計算該詞語在文章所出現的次數\n",
    "* idf ( inverse document frequency )：逆文檔頻率，衡量該字詞的重要性\n",
    "\n",
    "![tf-idf](https://cdn-images-1.medium.com/max/800/1*n1ReZe2OGaEBYg3kvzYhkQ.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "字詞的重要性隨著它在檔案中出現的次數成正比增加，但同時會隨著它在語料庫中出現的頻率成反比下降。也就是說，當 tf 值很高，而 df 值很低，這時得到的權重值就能夠把常見的關鍵字濾掉。詳細數學原理可參考[維基百科](https://zh.wikipedia.org/wiki/Tf-idf)。\n",
    "\n",
    "以五月天「乾杯」這首歌詞簡單舉例：\n",
    "\n",
    "![乾杯](https://cdn-images-1.medium.com/max/800/1*8W7A8cop8s_AA5NfqSh2Og.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Building prefix dict from D:\\ml\\w2v_collection\\lyrics_application\\model\\extra_dict\\dict.txt.big ...\n",
      "Loading model from cache C:\\Users\\pc\\AppData\\Local\\Temp\\jieba.uf510b40f8caa564909e705b4629077b5.cache\n",
      "Loading model cost 2.709 seconds.\n",
      "Prefix dict has been built succesfully.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不會,3290\n",
      "歲月,2741\n",
      "時間,2741\n",
      "回憶,2741\n",
      "一天,2715\n",
      "終點,2193\n",
      "懷念,2193\n",
      "也許,2193\n",
      "舉起,2193\n",
      "悠悠的,2169\n"
     ]
    }
   ],
   "source": [
    "# 設定繁體中文字典\n",
    "jieba.set_dictionary(JIEBA_DICTFILE_PATH)\n",
    "\n",
    "# 將\"乾杯\"這首歌的關鍵字詞取出來 \n",
    "# topK代表要取的前幾大的關鍵字 \n",
    "\n",
    "song_lyrics = os.path.join(DATASET_PATH, \"single_song.txt\")\n",
    "\n",
    "with open(song_lyrics, \"rb\") as f:\n",
    "    for line in f:\n",
    "        tags = jieba.analyse.extract_tags(line,topK=10, withWeight=True)\n",
    "        for tag, weight in tags:\n",
    "            print(tag + \",\" + str(int(weight * 10000)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 從歌詞萃取重要關鍵字\n",
    "\n",
    "接下來將演唱會歌單出現的 33 首歌詞同樣以 `jieba.analyse.extract_tags`取出歌曲的 10 大關鍵字，透過萃取出來的資訊，其實不難猜出下面這 5 首歌的歌名(你的腦海裡應該開始出現著這首歌的旋律了)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "不會,歲月,時間,回憶,一天,終點,懷念,也許,舉起,悠悠的\n",
      "\n",
      "我們,從不,那個,相識,出現,場景,哪裡,不曾,相遇,每秒\n",
      "\n",
      "我們,好好,變老,不過,一隻,沒有,喧囂,時間,最後,知道\n",
      "\n",
      "後來,我們,人生,無論是,淚水,一個,期待,中能,然後,快樂\n",
      "\n",
      "入陣曲,滾燙,無悔,夜未央,倖存,天未亮,沙場,心未涼,淚未,什麼\n",
      "\n",
      "最好,day,一天,best,閉上眼,銀河,好了沒,瞬間,那天,地球\n",
      "\n",
      "OAOA,一個,短暫,人生,永遠,一種,現在,無止盡,搖滾樂,轉眼\n",
      "\n",
      "la,will,孤單,high,High,fine,everything,Tomorrow,alright,陪伴\n",
      "\n",
      "oh,party,hey,派對,go,動物,night,我們,let,lonely\n",
      "\n",
      "什麼,能夠,拯救,愛情,結局,殘酷,束手無策,攻擊,那麼,地球\n",
      "\n",
      "決定,快樂,保護色,永遠,靈魂,軀殼,不愛了,鎖上,真正,不恨\n",
      "\n",
      "一個,兄弟,這樣,我們,怎樣,一次,身旁,人生,抬頭,變成\n",
      "\n",
      "過嗎,人生,沒有,部門,請假,目標,無限,富貴榮華,飛黃騰達,有限\n",
      "\n",
      "盡頭,怎麼樣,我們,終點,可有光,成名,那夜,一雙,那路,風光\n",
      "\n",
      "變成,交響,每個,孤單,崩壞,還有,一個,沒人,人唱,我心\n",
      "\n",
      "記得,卡片,西天,夥伴,掏耳朵,累不累,光榮,還在,回來,取經\n",
      "\n",
      "怎麼,擁有,快樂,盡頭,一陣風,感動,足夠,最痛,吹來,發現\n",
      "\n",
      "我們,無數,人們,未來,相異,羅盤,現在,命運,如海,怎樣\n",
      "\n",
      "yeah,出現,有沒有,呼喚,越來越,情節,一種,電影,黎明,我們\n",
      "\n",
      "頑固,一次,深處,活在,拼回,淚滴,一天,吞下,相信,自己\n",
      "\n",
      "do,ever,shine,die,現在,try,出來,jalalala,jalalan,時代\n",
      "\n",
      "夢寐以求,人們,改變,何時,看見,擁有,容顏,問你,春天,就算\n",
      "\n",
      "煩惱,噗通,甩掉,跳越,丟掉,地球,一顆,一瞬間,忘掉,再也\n",
      "\n",
      "love,ing,戀愛,改變,黃昏,噴射機,happy,黎明,心情,心跳\n",
      "\n",
      "我們,任意,那個,走過,唱片,無盡,歲月,光輝,搭著,遺忘\n",
      "\n",
      "有沒有,回憶,原來,飛翔,自傳,遺忘,愛情,最愛,最終,身旁\n",
      "\n",
      "決定,什麼,傷心,之後,就讓,快樂,潮落,潮起,拋棄,整個\n",
      "\n",
      "動次,快樂,oh,不管,慢歌,反正,音樂,趴著,活著,傷心\n",
      "\n",
      "一層,洋蔥,空氣,永遠,願意,剝開,聽到,沉默,偷偷,如果\n",
      "\n",
      "溫柔,沒有,這是,自由,我給,什麼,孤單,就讓,不明,想要\n",
      "\n",
      "突然,那麼,回憶,好想你,我們,聽到,空氣,翻滾,關心,過得\n",
      "\n",
      "倔強,瘋狂,大聲,絕望,一次,握緊,雙手,驕傲,最後,絕對\n",
      "\n",
      "總會,來作,甘願,講過,米蟲,憨人,要安,的話,夢中,頭腦\n",
      "\n",
      "已經,感覺,麥閣,無人,最好,這凍止,傷心,沒愛我,這愛你,免愛我\n",
      "\n",
      "無望,love,這款,法度,將阮來,寒到,作眠,別人,那會,將我放\n",
      "\n",
      "轉彎,我要,未來,現在,放棄,放縱,規則,放空,光年,盛夏\n"
     ]
    }
   ],
   "source": [
    "# 把每首歌的tags取出\n",
    "all_songs_lyrics = os.path.join(DATASET_PATH, \"test1.txt\")\n",
    "\n",
    "with open(all_songs_lyrics, \"rb\") as f:\n",
    "    for line in f:\n",
    "        tags = jieba.analyse.extract_tags(line,10)\n",
    "        print(\",\".join(tags))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "上述是看個別歌曲的關鍵字；接著我們來看這次五月天在「人生無限公司巡迴演唱會」所唱的歌單裡的含有哪些元素："
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "我們,快樂,現在,一個,什麼,沒有,回憶,傷心,孤單,永遠,人生,oh,變成,決定,怎樣\n"
     ]
    }
   ],
   "source": [
    "# 演唱會歌單出現的 33 首歌詞\n",
    "all_songs_lyrics = os.path.join(DATASET_PATH, \"test1.txt\")\n",
    "\n",
    "#每首歌的前10大tags的集合\n",
    "all_songs_top10_tags = [] \n",
    "\n",
    "with open(all_songs_lyrics, \"r\", encoding='utf8') as f1:\n",
    "    for line in f1:\n",
    "        words = jieba.analyse.extract_tags(line,10) #取10個tags\n",
    "        all_songs_top10_tags.extend(words) # 把tags存放起來\n",
    "f1.close()\n",
    "\n",
    "# 串接所有歌的前10大tags成一長字串\n",
    "all_songs_top10_tags_text = \" \".join(all_songs_top10_tags)\n",
    "\n",
    "# 最後再找出所有歌的前15大的tags\n",
    "top15_tags = jieba.analyse.extract_tags(all_songs_top10_tags_text,15) \n",
    "print(\",\".join(top15_tags))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 使用 wordcloud 呈現\n",
    "\n",
    "`wordcloud` 這個套件是文字雲，預設是支援英文(因為是 amueller 這位作者開發的)，需另外下載中文字體，才能在文字雲當中顯現中文，若是英文歌詞則無需特別下載字體。可參考 [Google Noto Fonts](https://www.google.com/get/noto/) 開源字體下載，選擇支援中文的字型，比如[NotoSansCJK-TC](https://noto-website-2.storage.googleapis.com/pkgs/NotoSansCJKtc-hinted.zip)。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MudPN7El1aqVQwMDm2DO2EEOHR6+ppts+WUldQOWXL8M8daMXb2X9Ebbj0gPR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\ne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ELA0AyS3yaROknI3dLTqrpMRV1tPuvwg+EmIeC6xEX87cxVu+PUzZJAVWi4cOfUGtpl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\nAi/IEtEXTT9DYfkrpOJzsNj2I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      "text/plain": [
       "<matplotlib.figure.Figure at 0x4cd0390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x48c90b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 設定停用字(排除常用詞、無法代表特殊意義的字詞)\n",
    "stopwords = {}.fromkeys([\"沒有\",\"一個\",\"什麼\",\"那個\"])\n",
    "\n",
    "# 產生文字雲\n",
    "wc = WordCloud(font_path=\"fonts/NotoSansCJKtc-Black.otf\", #設置字體\n",
    "               background_color=\"black\", #背景顏色\n",
    "               max_words = 2000 ,        #文字雲顯示最大詞數\n",
    "               stopwords=stopwords)      #停用字詞\n",
    "\n",
    "wc.generate(all_songs_top10_tags_text)\n",
    "\n",
    "# 視覺化呈現\n",
    "plt.imshow(wc)\n",
    "plt.axis(\"off\")\n",
    "plt.figure(figsize=(10,6), dpi = 100)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 4. 小結\n",
    "\n",
    "透過「從歌詞文本當中找出關鍵字 +文字雲視覺化」的關鍵字歌詞呈現，我們從五月天這 4 場「人生無限公司巡迴演唱會」共 33 首歌曲當中看到的結果：以「我們」、「人生」、「回憶」、「快樂」為主軸。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### 參考:\n",
    "![Youngmi huang](https://cdn-images-1.medium.com/fit/c/60/60/0*VYaQ-JgWF7OsETaG.jpg)\n",
    "\n",
    "* [Youngmi huang@以jieba與gensim探索文本主題：五月天人生無限公司歌詞分析(I)](https://medium.com/pyladies-taiwan/%E4%BB%A5-jieba-%E8%88%87-gensim-%E6%8E%A2%E7%B4%A2%E6%96%87%E6%9C%AC%E4%B8%BB%E9%A1%8C-%E4%BA%94%E6%9C%88%E5%A4%A9%E4%BA%BA%E7%94%9F%E7%84%A1%E9%99%90%E5%85%AC%E5%8F%B8%E6%AD%8C%E8%A9%9E%E5%88%86%E6%9E%90-i-cd2147b89083)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
